Grist et al. team trained computers to analyse brain images from children for identification of tumours. They’ve shown that applying analytical methods to enable machine distinguishes between the entire brain area and the tumour area in the images more than 80% improves how machine analyses the image to identify exact tumour area.

Abstract

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children’s brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality.

The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types.

The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.